Our research topic is Trend of Major Types of Crimes commited by White Males in the DC Area in 2016-2021. We chose this topic because we are interested in the impact of COVID-19 on crimes. We will use the data provided by the Metropolitan Police of DC regarding adult arrests over a time period stretching between 2016-2021.
We read the data .CSV files of adults arrest in DC area from 2016-2021
The column names of the data in 2016 and 2017 were not the same with others. The below table shows the column names of the data in 2016 and the data in 2016.
| col # | 2016 | 2018 |
|---|---|---|
| 1 | Arrestee.Type | Arrestee.Type |
| 2 | Arrest.Year | Arrest.Year |
| 3 | Arrest.Date | Arrest.Date |
| 4 | Arrest.Hour | Arrest.Hour |
| 5 | CCN | CCN |
| 6 | Arrest.Number. | Arrest.Number. |
| 7 | Age | Age |
| 8 | Defendant.PSA | Defendant.PSA |
| 9 | Defendant.District | Defendant.District |
| 10 | Defendant.Race | Defendant.Race |
| 11 | Defendant.Ethnicity | Defendant.Ethnicity |
| 12 | Defendant.Sex | Defendant.Sex |
| 13 | Arrest.Category | Arrest.Category |
| 14 | Charge.Description | Charge.Description |
| 15 | Arrest.Location.PSA | Arrest.Location.PSA |
| 16 | Arrest.Location.District | Arrest.Location.District |
| 17 | Arrest.Location.Block.GeoX | Arrest.Block.GEOX |
| 18 | Arrest.Location.Block.GeoY | Arrest.Block.GEOY |
| 19 | Offense.GEOY | Arrest.Latitude |
| 20 | Offense.GEOX | Arrest.Longitude |
| 21 | Offense.PSA | Offense.Location.PSA |
| 22 | Offense.District | Offense.Location.District |
| 23 | Arrest.Latitude | Offense.Block.GEOX |
| 24 | Arrest.Longitude | Offense.Block.GEOY |
| 25 | Offense.Latitude | Offense.Latitude |
| 26 | Offense.Longitude | Offense.Longitude |
The column names were same from the first column to the 14th column
in both data. On the other hand, the name and order of 15th and latter
columns were a bit different in those data. The latter columns were
about locations, and we were not very interested in the detail location.
Therefore, we deleted the latter columns except for the 16th and 22nd
columns. In addition, we dropped CNN (col #5) and
Arrest.Number. (col #6) because they were IDs and useless
for our analysis.
The format of date was different from years; the data in 2016 and 2017 has the format like , the data in 2018 to 2020 has the format like , and the data in 2021 has the format like . We coverted Since different date formats for different years are difficult to analyze, we will unify the date format to “yyyy-mm-dd”.
After deleting some columns and changing the date format, we binded data frames by rows.
To see whether there were abnormal values, we created the table showing some statistics for numerical variables.
| Arrest.Year | Arrest.Hour | Age | |
|---|---|---|---|
| Min | Min. :2016 | Min. : 0.00 | Min. : 18.00 |
| Q1 | 1st Qu.:2017 | 1st Qu.: 6.00 | 1st Qu.: 25.00 |
| Median | Median :2018 | Median :12.00 | Median : 32.00 |
| Mean | Mean :2018 | Mean :11.81 | Mean : 35.19 |
| Q3 | 3rd Qu.:2019 | 3rd Qu.:18.00 | 3rd Qu.: 43.00 |
| Max | Max. :2021 | Max. :23.00 | Max. :121.00 |
The maximum age was too old. 55 rows were assigned an age of over 100 years (117-121 ) in these data, and it seemed to be wrong. Therefore, we dropped these rows.
## [1] "Arrestee_Type" "Arrest_Year"
## [3] "Arrest_Date" "Arrest_Hour"
## [5] "Age" "Defendant_PSA"
## [7] "Defendant_District" "Defendant_Race"
## [9] "Defendant_Ethnicity" "Defendant_Sex"
## [11] "Arrest_Category" "Charge_Description"
## [13] "Arrest_Location_District" "Offense_Location_District"
## Arrestee_Type Arrest_Year Arrest_Date
## 0 0 0
## Arrest_Hour Age Defendant_PSA
## 0 0 29093
## Defendant_District Defendant_Race Defendant_Ethnicity
## 9337 0 0
## Defendant_Sex Arrest_Category Charge_Description
## 0 12 15
## Arrest_Location_District Offense_Location_District
## 184 11
## [1] "Arrestee_Type" "Arrest_Year"
## [3] "Arrest_Date" "Month"
## [5] "Day" "Arrest_Hour"
## [7] "Age" "Defendant_PSA"
## [9] "Defendant_District" "Defendant_Race"
## [11] "Defendant_Ethnicity" "Defendant_Sex"
## [13] "Arrest_Category" "Charge_Description"
## [15] "Arrest_Location_District" "Offense_Location_District"
## Warning: 程辑包'lubridate'是用R版本4.2.2 来建造的
##
## 载入程辑包:'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
Arrest_Category had some different values for 2021 and
other years:
Therefore, we coverted these values in 2021 into the correspond values in other years.
Since we were interested in crimes committed by while males, we
dropped rows where the value of Defendant_Race was not
“White”. The structure of the final data is shown in the below
table.
| column_name | class | first_values |
|---|---|---|
| Arrestee_Type | character | Adult Arrest, Adult Arrest, Adult Arrest, Adult Arrest, Adult Arrest, Adult Arrest |
| Arrest_Year | integer | 2016, 2016, 2016, 2016, 2016, 2016 |
| Arrest_Date | double | 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01 |
| Month | integer | 01, 01, 01, 01, 01, 01 |
| Day | integer | 1, 1, 1, 1, 1, 1 |
| Arrest_Hour | integer | 0, 0, 1, 1, 13, 2 |
| Age | integer | 39, 27, 27, 26, 48, 25 |
| Defendant_PSA | character | Out of State, Out of State, Out of State, Out of State, 404, Out of State |
| Defendant_District | character | Out of State, Out of State, Out of State, Out of State, 4D, Out of State |
| Defendant_Race | integer | WHITE, WHITE, WHITE, WHITE, WHITE, WHITE |
| Defendant_Ethnicity | character | UNKNOWN, NOT HISPANIC, HISPANIC, NOT HISPANIC, NOT HISPANIC, HISPANIC |
| Defendant_Sex | integer | MALE, MALE, MALE, MALE, MALE, MALE |
| Arrest_Category | character | Simple Assault, Simple Assault, Driving/Boating While Intoxicated, Simple Assault, Simple Assault, Simple Assault |
| Charge_Description | character | Threats To Do Bodily Harm -misd, Simple Assault, Driving While Intoxicated -2nd Off, Simple Assault, Simple Assault, Simple Assault |
| Arrest_Location_District | integer | 2D, 3D, 4D, 5D, 1D, 3D |
| Offense_Location_District | integer | 2D, 3D, 4D, 5D, 1D, 3D |
| Weekday | integer | 星期五, 星期五, 星期五, 星期五, 星期五, 星期五 |
## # A tibble: 24 × 2
## Arrest_Hour Total
## <int> <int>
## 1 0 5681
## 2 1 7425
## 3 2 6769
## 4 3 6286
## 5 4 5425
## 6 5 4412
## 7 6 4465
## 8 7 6101
## 9 8 6729
## 10 9 6594
## # … with 14 more rows
## # A tibble: 31 × 2
## Day Total
## <int> <int>
## 1 1 5516
## 2 2 5193
## 3 3 5240
## 4 4 5138
## 5 5 5189
## 6 6 4998
## 7 7 4878
## 8 8 5022
## 9 9 4947
## 10 10 5090
## # … with 21 more rows
## # A tibble: 7 × 3
## Weekday Total Percent
## <fct> <int> <dbl>
## 1 星期一 19553 12.8
## 2 星期二 21418 14.1
## 3 星期三 23520 15.4
## 4 星期四 23241 15.3
## 5 星期五 23189 15.2
## 6 星期六 22142 14.5
## 7 星期日 19268 12.6
## # A tibble: 12 × 3
## Month Total Percent
## <fct> <int> <dbl>
## 1 01 12751 8.37
## 2 02 12158 7.98
## 3 03 13625 8.94
## 4 04 12344 8.10
## 5 05 13427 8.81
## 6 06 12729 8.36
## 7 07 13008 8.54
## 8 08 12991 8.53
## 9 09 12578 8.26
## 10 10 13029 8.55
## 11 11 11869 7.79
## 12 12 11822 7.76
## # A tibble: 6 × 3
## Arrest_Year Total Percent
## <fct> <int> <dbl>
## 1 2016 29980 19.7
## 2 2017 31209 20.5
## 3 2018 29100 19.1
## 4 2019 27915 18.3
## 5 2020 18479 12.1
## 6 2021 15648 10.3
This plot is to compare the age of people who get arrested with each different year. From the plot, we can see that there are lots of outliers. We need to get rid of the outliers first.
## [1] 151552 17
## [1] 152331 17
Removed 834 outliers.
After remove the ourliers, we could clearly see that as year goes up,
the minimum age goes up a little bit. The maximum age from this sample
goes down. The median is pretty much same compare to different
years.
There are less younger criminals as year passing from 2016 to 2020 based on this sample. Criminal with age from 30 to 35 arrested more than other ages, which also probably means that there are more offenders with age from 30 to 35.
Why boxplot? The advantage of consider median over sample mean is that it is less affected by extreme observations.
## [1] WHITE BLACK UNKNOWN ASIAN MULTIPLE OTHER
## Levels: ASIAN BLACK MULTIPLE OTHER UNKNOWN WHITE
## [1] 15739
## [1] 131385
## [1] 897
## [1] "Simple Assault" "Assault on a Police Officer"
## [3] "Traffic Violations" "Weapon Violations"
## [5] "Driving/Boating While Intoxicated" "Narcotics"
## [7] "Disorderly Conduct" "Theft"
## [9] "Liquor Law Violations" "Other Crimes"
## [11] "Theft from Auto" "Offenses Against Family & Children"
## [13] "Assault with a Dangerous Weapon" "Release Violations/Fugitive"
## [15] "Motor Vehicle Theft" "Damage to Property"
## [17] "Sex Abuse" "Property Crimes"
## [19] "Vending Violations" "Robbery"
## [21] "Aggravated Assault" "Burglary"
## [23] "Sex Offenses" "Fraud and Financial Crimes"
## [25] "Prostitution" "Homicide"
## [27] "Kidnapping" "Gambling"
## [29] "Arson" NA
From the box plot, we can see that most sex abuse happens around 10am to 13pm. The sex abuse happens all the time and it changes with different years.
From the box plot, we can see that most theft happens around 15pm and they all super same with each year except year 2019. The theft always happening from 11am to 19 pm. That’s a funny fact.
Sex distribution
Race distribution
From the barplot of Sex, we can see there is not enough
female samples. From the barplot of Race, there are too
many black people and to small other races which is not appropriate for
us to do analysis. Therefore, we choose to investigate in white male
crimes.
same pattern here as above, will dig into a few other stuff too..
## # A tibble: 31 × 2
## Day Total
## <int> <int>
## 1 1 524
## 2 2 404
## 3 3 403
## 4 4 395
## 5 5 405
## 6 6 455
## 7 7 405
## 8 8 376
## 9 9 399
## 10 10 430
## # … with 21 more rows
## # A tibble: 7 × 3
## Weekday Total Percent
## <fct> <int> <dbl>
## 1 星期一 1566 12.8
## 2 星期二 1463 11.9
## 3 星期三 1639 13.3
## 4 星期四 1736 14.1
## 5 星期五 1876 15.3
## 6 星期六 2046 16.7
## 7 星期日 1952 15.9
## # A tibble: 12 × 3
## Month Total Percent
## <fct> <int> <dbl>
## 1 01 1189 9.68
## 2 02 998 8.13
## 3 03 1125 9.16
## 4 04 921 7.50
## 5 05 1058 8.62
## 6 06 1010 8.23
## 7 07 966 7.87
## 8 08 961 7.83
## 9 09 1023 8.33
## 10 10 1108 9.02
## 11 11 991 8.07
## 12 12 928 7.56
## # A tibble: 6 × 3
## Arrest_Year Total Percent
## <fct> <int> <dbl>
## 1 2016 2620 21.3
## 2 2017 2636 21.5
## 3 2018 2297 18.7
## 4 2019 2191 17.8
## 5 2020 1425 11.6
## 6 2021 1109 9.03
## # A tibble: 10 × 2
## Arrest_Category Total
## <chr> <int>
## 1 Simple Assault 2661
## 2 Traffic Violations 1548
## 3 Release Violations/Fugitive 1133
## 4 Driving/Boating While Intoxicated 1045
## 5 Other Crimes 821
## 6 Theft 674
## 7 Narcotics 654
## 8 Liquor Law Violations 562
## 9 Disorderly Conduct 433
## 10 Damage to Property 414
## `summarise()` has grouped output by 'Arrest_Year'. You can override using the
## `.groups` argument.
## # A tibble: 10 × 3
## # Groups: Arrest_Year [1]
## Arrest_Year Arrest_Category Total
## <fct> <chr> <int>
## 1 2016 Aggravated Assault 23
## 2 2016 Assault on a Police Officer 42
## 3 2016 Assault with a Dangerous Weapon 73
## 4 2016 Burglary 25
## 5 2016 Damage to Property 98
## 6 2016 Disorderly Conduct 83
## 7 2016 Driving/Boating While Intoxicated 206
## 8 2016 Fraud and Financial Crimes 11
## 9 2016 Homicide 2
## 10 2016 Kidnapping 4
We see there are some wired high peaks in the count plots above. This is
due to some protests in DC area which caused the surge of crimes. If we
remove these peaks, they will be simliar to other plots, but we cannot,
these are related to the events happened in the real world.
## [1] 2D 3D 4D 5D 1D 7D 6D UNKNOWN <NA>
## [10]
## Levels: 1D 2D 3D 4D 5D 6D 7D UNKNOWN
##
## 1D 2D 3D 4D 5D 6D 7D UNKNOWN
## 22 1858 3053 2555 2816 1231 437 260 29
## [1] 2D 3D 4D 5D 1D 7D 6D #N/A UNKNOWN
## [10] Unk
## Levels: #N/A 1D 2D 3D 4D 5D 6D 7D Unk UNKNOWN
##
## #N/A 1D 2D 3D 4D 5D 6D 7D Unk UNKNOWN
## 12 2044 3100 2531 2719 1195 413 238 16 10
## `summarise()` has grouped output by 'Arrest_Location_District'. You can
## override using the `.groups` argument.
## Selecting by Total
## `summarise()` has grouped output by 'Offense_Location_District'. You can
## override using the `.groups` argument.
## Selecting by Total
We created some bar plots to see the number of occurrences per type
of crime.
The Bar plots of crimes in each year are as follows:
“Offenses Against Family & Children” have been increasing after COVD-19.
The top 6 crimes (or 7 crimes when ‘Other Crimes’ are included) in each year are as follows.
| Rank | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|
| 1 | Simple Assault | Simple Assault | Simple Assault | Simple Assault | Simple Assault | Simple Assault |
| 2 | Traffic Violations | Traffic Violations | Traffic Violations | Traffic Violations | Driving/Boating While Intoxicated | Traffic Violations |
| 3 | Release Violations/Fugitive | Release Violations/Fugitive | Release Violations/Fugitive | Prostitution | Release Violations/Fugitive | Driving/Boating While Intoxicated |
| 4 | Driving/Boating While Intoxicated | Driving/Boating While Intoxicated | Driving/Boating While Intoxicated | Driving/Boating While Intoxicated | Traffic Violations | Release Violations/Fugitive |
| 5 | Liquor Law Violations | Other Crimes | Narcotics | Release Violations/Fugitive | Offenses Against Family & Children | Other Crimes |
| 6 | Narcotics | Disorderly Conduct | Theft | Other Crimes | Other Crimes | Offenses Against Family & Children |
| 7 | NA | Liquor Law Violations | NA | Theft | Narcotics | Damage to Property |
To see the trend of the above major crimes, we created a line plot as follows.
“Simple Assault”, “Traffic Violations”, and “Theft” have clearly declined since 2020. On the other, “Offenses Against Family & Children” has increased in 2020 and 2021 compared to previous years. COVID-19 seems to be related to these trend change. We posed the following SMART QUESTION, and we will analyze these four crimes in detail in the following.
Is there a significant difference in “Simple Assault”, “Traffic Violations”, “Theft”, and “Offenses Against Family & Children” trends among adult white males within the DC area between 2016 and 2021, and could COVID protocols play a role in these trend shifts?
group white males by age:
Young: 18-29; Middle age: 30-50; Old: >50. Crimes:“Simple Assault”,“Release Violations/Fugitive”,“Traffic Violations”, “Narcotics”, “Theft”, “Other Crimes”
Release Violations/Fugitive
Traffic Violations
Narcotics
THEFT
Other Crimes
## year Simple.Assault Release.Violations.Fugitive Traffic.Violations Narcotics
## 1 2016 232 94 171 171
## 2 2017 217 123 137 137
## 3 2018 191 93 129 129
## 4 2019 212 84 112 112
## 5 2020 146 58 42 42
## 6 2021 114 30 47 47
## Theft Other.Crimes
## 1 57 63
## 2 71 88
## 3 60 53
## 4 59 75
## 5 20 34
## 6 21 25
Some plots of crime trend of different generation
##
## 载入程辑包:'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
Barplot of crime trend of white males of different generations
Plot each crime separately:
Plot all 6 crimes together but with different age groups
Though the samples of three generations (young middle age, old people)
are different, the behaviors of each crime trend plots are all the same.
There is no obvious difference among three groups, thus we can see
“Age” do not have a significant effect on the trend of
these common crimes.
Since crime is likely to be a rare event, the number of occurrences per day of a given crime is expected to follow Poisson distribution. Poisson distribution is a distribution used to describe the distribution of the number of rare phenomena when a large number of them are observed. If a distribution follows Poisson distribution, and the average number of occurrences of the phenomenon is \(\lambda\), the probability that the phenomenon will occur \(x\) times is given by \[p(x) = \exp(-\lambda)\frac{\lambda^{x}}{x!}.\] In the following, we will estimate \(\lambda\) of each crime before and after COVID-19 to see there is a difference in crime trend.
The trend of “Offenses Against Family & Children,” Domestic Violence (DV), appears to have changed after COVID-19. The frequency table of DV before COVID-19 is as follows.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 1364 | 0.9336071 |
| 1 | 95 | 0.065024 |
| 2 | 2 | 0.0013689 |
| 3 | 0 | 0 |
We can calculate \(\lambda\) from the above table and \(\lambda = 0.0678\). We will plot the histogram and Poisson distribution with \(\lambda = 0.0678\) to check if they match or not.
We can see that the Poisson distribution fits well with the histogram.
Next, we try to estimate \(99\%\) Confidence Interval of \(\lambda\). The variance of Poisson distribution is equal to its mean (\(\lambda\)). Therefore, \(99\%\) Confidence Interval of \(\lambda\) can be written as \[ \bar{x} - z_{*}\cdot\sqrt{\frac{\bar{x}}{n}} \leq \lambda \leq \bar{x} + z_{*}\cdot\sqrt{\frac{\bar{x}}{n}}, \] where \(\bar{x}\) is the sample mean, \(n\) is the sample size, and \(z_*\) is z-value corresponding to the \(99\%\) confidence interval, and the value is 2.58. From this expression, 99% Confidence Interval of \(\lambda\) for DV before COVID-19 is [0.05, 0.0856].
The frequency table of DV after COVID-19 is as follows.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 680 | 0.9302326 |
| 1 | 47 | 0.0642955 |
| 2 | 1 | 0.001368 |
| 3 | 0 | 0 |
| 4 | 1 | 0.001368 |
| 5 | 0 | 0 |
| … | 0 | 0 |
| 44 | 0 | 0 |
| 45 | 1 | 0.001368 |
| 46 | 0 | 0 |
| … | 0 | 0 |
| 77 | 0 | 0 |
| 78 | 1 | 0.001368 |
| 79 | 0 | 0 |
| … | 0 | 0 |
There are two outliers (45 and 78) in the table. The dates of them are 1 and 1. Since these dates are correspond to “Capitol attack” and “George Floyd protests”, we will drop the value of these dates.
The calculated \(\lambda = 0.0725\). The histogram and the poisson distribution with \(\lambda = 0.0725\) are shown in Figure 16.
The Poisson distribution fits well with the histogram.
99% Confidence Interval of \(\lambda\) for DV after COVID-19 is [0.0465, 0.0985].
Figure 17 shows the Confidence Intervals before and after COVID-19. There was overlap in the Confidence Intervals, and it is not possible to say that there was a change in the \(\lambda\) of “Offenses Against Family & Children” before or after COVID-19.
The trend of “Traffic Violations” also appears to have changed after COVID-19. The frequency table of Traffic Violations before COVID-19 is as follows.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 602 | 0.4123288 |
| 1 | 530 | 0.3627652 |
| 2 | 225 | 0.1540041 |
| 3 | 77 | 0.0527036 |
| 4 | 22 | 0.0150582 |
| 5 | 4 | 0.0027379 |
| 6 | 1 | 6.844627^{-4} |
| 7 | 0 | 0 |
The calculated \(\lambda = 0.907\). The histogram and the poisson distribution with \(\lambda = 0.907\) are shown in Figure 19.
The Poisson distribution fits well with the histogram.
99% Confidence Interval of \(\lambda\) for Traffic Violations before COVID-19 is [0.842, 0.972].
The frequency table of Traffic Violations after COVID-19 is as follows.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 546 | 0.746922 |
| 1 | 156 | 0.2134063 |
| 2 | 23 | 0.0314637 |
| 3 | 3 | 0.004104 |
| 4 | 3 | 0.004104 |
| 5 | 0 | 0 |
The calculated \(\lambda = 0.306\). The histogram and the poisson distribution with \(\lambda = 0.306\) are shown in Figure 21.
The Poisson distribution fits well with the histogram.
99% Confidence Interval of \(\lambda\) for Traffic Violations before COVID-19 is [0.252, 0.358].
Figure 22 shows the Confidence Intervals before and after COVID-19. There was no overlap in the Confidence Intervals, and there may have been a change in the Traffic Violations lambda before and after COVID-19.
The below table shows the frequency and relative frequency of Simple Assault before COVID-19.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 438 | 0.2997947 |
| 1 | 479 | 0.3278576 |
| 2 | 284 | 0.1943874 |
| 3 | 156 | 0.1067762 |
| 4 | 76 | 0.0520192 |
| 5 | 13 | 0.008898 |
| 6 | 8 | 0.0054757 |
| 7 | 4 | 0.0027379 |
| 8 | 1 | 6.844627^{-4} |
| 9 | 2 | 0.0013689 |
| 10 | 0 | 0 |
We got \(\lambda = 1.36\) by calculating the average of occurrences per day.
The frequency and relative frequency in 2020 and 2021 is shown in below. The \(\lambda\) for 2020 and 2021 was \(0.923\).
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 297 | 0.4062927 |
| 1 | 267 | 0.3652531 |
| 2 | 112 | 0.1532148 |
| 3 | 43 | 0.0588235 |
| 4 | 8 | 0.0109439 |
| 5 | 3 | 0.004104 |
| 6 | 0 | 0 |
| 7 | 0 | 0 |
| 8 | 1 | 0.001368 |
| 9 | 0 | 0 |
Figure 26 shows the Confidence Intervals before and after COVID-19. There was no overlap in the Confidence Intervals, and there may have been a change in the Simple Assault lambda before and after COVID-19.
The frequency and relative frequency in 2016 to 2019 is shown in below. The \(\lambda\) before COVID-19 was \(0.404\).
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 973 | 0.6659822 |
| 1 | 398 | 0.2724162 |
| 2 | 79 | 0.0540726 |
| 3 | 10 | 0.0068446 |
| 4 | 1 | 6.844627^{-4} |
| 5 | 0 | 0 |
The frequency and relative frequency in 2020 and 2021 are shown in below. The \(\lambda\) for 2020 and 2021 was \(0.115\).
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 653 | 0.8932969 |
| 1 | 72 | 0.0984952 |
| 2 | 6 | 0.0082079 |
| 3 | 0 | 0 |
Figure 31 shows the Confidence Intervals before and after COVID-19. There was no overlap in the Confidence Intervals, and there may have been a change in the Theft lambda before and after COVID-19.
Statistically significant reductions in Simple Assault and Traffic Violations were observed for \(\lambda\) before and after COVID-19. Since these crimes seem to be more likely to occur the more people are out, it is likely that the restrictions and curbs on going out due to COVID-19 contributed to the decrease in these crimes.
A statistically significant decrease in theft was also observed in \(\lambda\) before and after Corona. Considering that thefts are committed against empty homes, the decrease in empty homes due to the curfew restrictions caused by COVID-19 may have contributed to the decrease in thefts.
The more time one spends at home due, the more Offenses Against Family & Children are likely to increase. In fact, in terms of the number of cases alone, Offenses Against Family & Children have increased after COVID-19. At first glance, the curfew restrictions caused by COVID-19 seems to be the cause. However, most of these cases were caused by special incidents unrelated to COVID-19, and when these effects were removed, there was no statistically significant difference in the change in Offenses Against Family & Children before and after COVID-19. As for white males in the DC area, Offenses Against Family & Children to the point of arrest does not appear to be affected by the changes in their lives caused by COVID-19.
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 817 | 0.559206 |
| 1 | 428 | 0.29295 |
| 2 | 168 | 0.1149897 |
| 3 | 40 | 0.0273785 |
| 4 | 5 | 0.0034223 |
| 5 | 3 | 0.0020534 |
| 6 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 549 | 0.751026 |
| 1 | 154 | 0.2106703 |
| 2 | 24 | 0.0328317 |
| 3 | 4 | 0.005472 |
| 4 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 875 | 0.5989049 |
| 1 | 416 | 0.2847365 |
| 2 | 138 | 0.0944559 |
| 3 | 25 | 0.0171116 |
| 4 | 6 | 0.0041068 |
| 5 | 1 | 6.844627^{-4} |
| 6 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 527 | 0.7209302 |
| 1 | 168 | 0.2298222 |
| 2 | 29 | 0.0396717 |
| 3 | 5 | 0.0068399 |
| 4 | 2 | 0.002736 |
| 5 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 1053 | 0.7207392 |
| 1 | 318 | 0.2176591 |
| 2 | 66 | 0.0451745 |
| 3 | 10 | 0.0068446 |
| 4 | 5 | 0.0034223 |
| 5 | 6 | 0.0041068 |
| 6 | 0 | 0 |
| 7 | 1 | 6.844627^{-4} |
| 8 | 0 | 0 |
| 9 | 1 | 6.844627^{-4} |
| 10 | 0 | 0 |
| 11 | 0 | 0 |
| 12 | 0 | 0 |
| 13 | 1 | 6.844627^{-4} |
| 14 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 652 | 0.8919289 |
| 1 | 66 | 0.0902873 |
| 2 | 10 | 0.0136799 |
| 3 | 3 | 0.004104 |
| 4 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 1090 | 0.7460643 |
| 1 | 259 | 0.1772758 |
| 2 | 76 | 0.0520192 |
| 3 | 32 | 0.0219028 |
| 4 | 3 | 0.0020534 |
| 5 | 1 | 6.844627^{-4} |
| 6 | 0 | 0 |
| # of occurrences per day | Frequency | Relative frequency |
|---|---|---|
| 0 | 699 | 0.9562244 |
| 1 | 27 | 0.0369357 |
| 2 | 4 | 0.005472 |
| 3 | 1 | 0.001368 |
| 4 | 0 | 0 |